Abstract PS6-02: Spatially defined immune-related proteins and outcome in triple negative breast cancer in the FinXX trial and Mayo Clinic cohort
Bibliographic record
Abstract
Abstract Background: Growing data established the pivotal role of preexisting immune response in triple negative breast cancer (TNBC). Conventionally, preexisting immune response can be evaluated by quantifying tumor infiltrating lymphocytes mainly in the stroma or gene expression analysis from the whole tumor section. Due to technical challenges with these conventional methods, limited data regarding specific subtypes and spatial distribution of these immune infiltrates are currently available. Methods: NanoString IO360 gene expression analysis and Digital Spatial Profiling (DSP) were used. DSP was used to quantify 29 immune-related proteins in stromal and tumor-enriched segments from 44 TNBC samples from the FinXX trial (NCT00114816) and 335 samples from the Mayo Clinic (MC) cohort of centrally reviewed TNBC (Leon-Ferre BCRT 2018). In FinXX trial, 22 patients with recurrence and 22 patients without recurrence were included. In MC cohort, 217/335 patients received adjuvant chemotherapy while 118 patients had surgery only without adjuvant chemotherapy. Regions were segmented based on pancytokeratin staining. The general linear model was used for statistical analysis of differential expression with recurrence free survival (RFS) as a categorical variable (recur yes or no). Kaplan-Meier (KM) estimates and Cox regression models were also used for analysis. Results: In the FinXX trial, there were 12 out of 29 proteins in tumor epithelial segments (intraepithelial) which were significantly expressed at higher levels among patients who were free of recurrence. These proteins include Beta-2 microglobulin, CD11c, CD20, CD40, CD56, CD8, Granzyme B, HLA-DR, ICOS, PD-L1, PD-L2, and TGFB1. In contrast, merely 5 out of 29 proteins in stromal segments were significantly differentially expressed in these 2 groups of patients. Granzyme B, IDO1, PD-L1, and PD-L2 in stroma were significantly higher and SMA was significantly lower in patients without recurrence. Using Cox regression models, intraepithelial CD56, CD40, and HLA-DR were significantly associated with outcome. When comparing between highest and lowest intraepithelial protein expression by tertile, intraepithelial CD56 (HR 0.12, 95%CI 0.03-0.39, p < 0.001), CD40 (HR 0.13, 95%CI 0.04-0.46, p = 0.002), and HLA-DR (HR 0.24, 95%CI 0.06-0.89, p = 0.032) were significantly associated with improved outcome. However, expression of these same proteins in stroma was not associated with outcome. Using KM estimates, intraepithelial CD56 (p < 0.0001), CD40 (p = 0.0006), and HLA-DR (p = 0.013) were also significantly associated with improved outcome. Nonetheless, RNA expression of these proteins by IO360 from whole tumor sections were not significantly associated with outcome (CD56 p = 0.27, CD40 p = 0.21, HLA-DR p = 0.48). Similar findings with DSP were observed in MC TNBC cohort. Comparing between the highest and lowest quartiles, there were significantly fewer patients who developed recurrence with high protein expression of intraepithelial CD56 (p < 0.001), CD40 (p = 0.002), and HLA-DR (p = 0.006). Conclusions: Using an in-depth analysis with spatially defined context, we identify that there were numerically more intraepithelial immune-related proteins associated with outcome compared to proteins in stroma. Specifically, intraepithelial CD56, CD40, and HLA-DR were significantly associated with improved outcome in both FinXX and MC TNBC cohorts. However, neither expression of these proteins in stroma nor RNA expression from whole tumor were associated with outcome. Our study highlights the impact of spatial biology and the importance of evaluating each potential biomarker in a spatially defined manner. Support: W81XWH-15-1-0292, BCRF 19-161, P50CA116201-9, P50CA015083 Citation Format: Saranya Chumsri, Jodi M. Carter, Yaohua Ma, Douglas Hinerfeld, Heather Ann Brauer, Sarah Warren, Torsten O. Nielsen, Karama Asleh, Heikki Joensuu, Edith A. Perez, Roberto A. Leon-Ferre, David W. Hillman, Judy C. Boughey, Minetta C. Liu, James N. Ingle, Krishna R. Kalari, Fergus J. Couch, Keith L. Knutson, Matthew P. Goetz, E. A. Thompson. Spatially defined immune-related proteins and outcome in triple negative breast cancer in the FinXX trial and Mayo Clinic cohort [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS6-02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".